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i-manager's Journal on Software Engineering (JSE)

Current Issue Vol. 19 Issue 2

Most Cited

Volume 8 Issue 1 July - September 2013

Article

Role Of Software Re-Engineering Through C To Java Conversion

Vinod Kumar Yadav* , Ajay Pal Singh, Satendra Kumar*

* Assistant Professor, Department of Computer Science and Engineering, SLSET, U.K., India.

Assistant Professor, Department of Computer Science and Engineering, DBIT, UP, India.

* Assistant Professor, Department of Computer Science and Engineering, SITM, Haryana, India.

Yadav, V. K., Singh, A. P., and Kumar, S. (2013). Role Of Software Re-Engineering Through C To Java Conversion. i-manager’s Journal on Software Engineering, 8(1), 1-6. https://doi.org/10.26634/jse.8.1.2420

Abstract

Re-engineering is used to support evolutionary maintenance and it is most widely used in modern software development. The Re-engineering occurs at two level of abstraction: at business level and at software level. The term maintenance should be replaced by “re-engineering” or renovation or reclamation because it not only recovers information from existing system but uses this information to alter or reconstitute the existing system in an effect to improve its overall quality. The source code translation is one of the main tasks in software reengineering which is based on source to source transformation which plays an important role. If we want to migrate a procedural language into object oriented programming language because of its reusability of code, the code conversion is necessary due to hardware platform update, staff skill shortage and organization changes.

References

[1]. Roger S. Pressman, “Software Engineering: A Practitioner’s Approach”, fifth edition, McGraw Hill, international edition.

[4]. Cimitile Aniello, De Lucia Andrea, Giuseppe Antonio Di Lucca, Rita Anna & Fasolino (1999). Identifying objects in legacy systems using design metrics: Journal of Systems and Software, 44 (1999) 199-211© 1999 Elsevier science.

[5]. Tucker D. Casey, Simmonds M. Devon. (2010). “A Case study in software re-engineering” Seventh International Conference on Information Technology, page 1107-1112.

[6]. Demaine D. Erik, “C to Java: Converting Pointers into references”, Department of Computer Science, University of Waterloo, Canada.

[7]. Koschke Rainer, Cordy James, Jarzabek Stanislaw, Inoue Katsuro (2009). “Third International Workshop on Software Clones (IWSC)” 09 European Conference on Software Maintenance and Re-engineering, IEEE.

[8]. Indravadan S. Patel, Vandana A. Verma (2012). “Software Re-engineering Piracy Issues of Code Translation by End User”, International Journal of Research in IT & Management, Volume 2, Issue 2.

[9]. Martin Johannes & Muller A. Hausi (2001). “Strategies for Migration from C to Java”, IEEE, 25th European conference.

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Research Paper

Performance Evaluation Of Fuzzy Voters Used In Safety Critical Systems

Phani Kumar* , SeethaRamaiah Panchumarthy, Anu A.Gokhale*

* Professor & Head, Department of CSE, GITAM University, Hyderabad Campus, Andhra Pradesh, India.

Professor, Department of CS & SE, Andhra University College of Engineering, Visakhapatnam, India.

* Professor & Coordinator, Computer Systems Technology, Department of Technology, Illinois State University.

Singamsetty, P., Panchumarthy, S., and Anu A. Gokhale, A. A. (2013). Performance Evaluation Of Fuzzy Voters Used In Safety Critical Systems. i-manager’s Journal on Software Engineering, 8(1), 7-23. https://doi.org/10.26634/jse.8.1.2421

Abstract

The main objective of this research paper is to design a self configurable dynamic fuzzy voter that can be used in the NModular Redundant (NMR) systems to mask the fault and exhibit better safety performance. Proposed dynamic voter can configure itself for the changing data of any range and changing deviations in the redundant module outputs due to the changes in the operational modes of the safety- critical embedded systems while in operation. In this paper, existing fuzzy voters are extensively surveyed and the merits and demerits or limitations are discussed. The major limitation observed in the existing fuzzy voters is the preselected optimal static fuzzy parameter selection technique. Preselected optimal fuzzy parameters may work for only specific ranges of data and specific amount of deviations among the redundant modules outputs of NMR systems but fail for the changing data ranges and deviations conditions. A dynamic fuzzy parameter selection method using statistical parameters on the local data in each voting cycle is proposed in this paper and applied to the existing static fuzzy voters. Static and dynamic versions of the fuzzy voters are empirically evaluated for 10,000 voting cycles and safety performance is plotted. Dynamic voters have shown improved safety performance compared to their static counterparts and are more useful for the changing operational modes and conditions.

References

[1]. Z. Georgiev, M. Stojcev (1994). VLSI common voting module for fault-tolerant TMR system in industrial system control applications, Internat. J. Electron. 76 (2) 163–205.

[2]. B.W. Johnson (1989). Design and Analysis of Fault-Tolerant Digital Systems, Addison Wesley, Reading, MA.

[3]. M.K. Stojcev, J.Lj. Djordgevic, M.D. Krstic (2001). A hardware mid-value select voter architecture, Microelectron. J. 32 149–162.

[4]. J.M. Bass, P.R. Croll, P.J. Fleming, L.J.C. Woolliscroft (1994). Three domain voting in real-time distributed control systems, Proc. 2nd Euromicro Workshop on Parallel and Distributed Processing, pp. 317–324.

[5]. K. Kim, M.A. Vouk, D.E. McAllister (1996). An empirical evaluation of maximum likelihood voting in failure correlation conditions, Proc. ISSRE'96 pp. 330–339.

[6]. K. Kim, M.A. Vouk, D.F. McAllister (1998). Fault tolerant software voters based on fuzzy equivalence relations, Proc. IEEE Aerospace Conf. 4 ,pp. 5–19.

[7]. G. Latif-Shabgahi (1999). Performance analysis of software implemented inexact voting algorithms, Ph.D. Thesis, Department of Automatic Control and Systems Engineering, The University of SheKeld, SheKeld, UK.

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[9]. P.R. Lorczak, A.K. Caglayan & D.E. Eckhardt (1989). A Theoretical Investigation of Generalized Voters for Redundant Systems, presented at FTCS-19. Digest of Papers, Nineteenth International Symposium on Fault-Tolerant Computing, Chicago, USA.

[10]. M. Tagvaei (2001). Experimental evaluation of voting algorithms, M.Sc. Thesis, Automatic Control and Systems Department, The University of SheKeld, SheKeld, UK.

[11]. A.D. De Leon (2003). Voting algorithms, M.Sc. Thesis, Department of Automatic Control and Systems Engineering Department, The University of SheKeld, SheKeld, UK.

[12]. G. Latif-Shabgahi & A.J. Hirst, (2005). A fuzzy voting scheme for hardware and software fault tolerant systems, Fuzzy Sets and Systems, 150 (3) 579–598.

[13]. R. Hoseinnezhad & A. Bab-Hadiashar (2006). Fusion of redundant information in brake-by- wire systems using a fuzzy voter. Journal of Advances in Information Fusion,1(1):pp. 52–62.

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[17]. D.M. Blough & G.F. Sullivan (1990). A comparison of voting strategies for fault-tolerant distributed systems, in Proc. IEEE 9th Symp. Reliable Distributed Systems, Huntsville, Alabama, pp. 136–145.

[18]. G. Latif-Shabgahi (2004). A Novel Algorithm for Weighted Average Voting Used in Fault-Tolerant Computing Systems, Microprocessors and Microsystems, Vol. 28, pp. 357-361.

[19]. G. Latif-Shabgahi, J.M. Bass, & S. Bennett (2001). History-Based Weighted Average Voter: A Novel Software Voting Algorithm for Fault-Tolerant Computer Systems, Euromicro Conference on Parallel, Distributed, and Network-Based Processing, pp. 402-409.

[20]. G. Latif-Shabgahi, Julian M. Bass & Stuart Bennett (2004). A taxonomy for software voting algorithms used in safety-critical systems, IEEE Trans. Reliability, vol. 53, no. 3, pp 319-328.

[21]. G. Latif-Shabgahi & S. Bennett (1999). Adaptive majority voter: a novel voting algorithm for real-time fault-tolerant control systems, in 25th Euromicro Conf., vol. 2, pp. 113-120.

[22]. S. Phani Kumar, P.S.Ramaiah & V.Khanaa (2011). Architectural Patterns to Design Software Safety based Safety-Critical Systems, Proceedings of ICCCS' 11 International Conference on Communication, Computing & Security, pp:620-623 ACM New York, NY, USA ©2011 ISBN: 978-1-4503-0464-1 doi>10.1145/1947940.1948069.

[23]. M. Das, & S. Battacharya (2010). A Modified History Based Weighted Average Voting with Soft-Dynamic Threshold, in ACE '10, International Conference on Advances in Computer Engineering, ISBN: 978-0-7695-4058-0 doi>10.1109/ACE.2010.45.

[24]. Zarafshan, F, Latif-Shabgahi & G.R., Karimi (2010). “A Novel Weighted Voting algorithm based on Neural Networks for Fault-Tolerance Systems”, ICCSIT, IEEE International Conference on Computer Science and Information Technology, pp.135-139 doi>10.1109/ICCSIT.2010. 5565122.

[25]. PhaniKumar Singamsetty & Seetha Ramaiah Panchumarthy (2011). A Novel History based Weighted Voting Algorithm for Safety Critical Systems, Special Issue on Advanced Algorithms in Journal of Advances in Information Technology, ISSN 1798-2340. Vol. 2, Issue 3, pp.139 – 145.

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Research Paper

The Software Architecture Towards Performance Evolution

R. Aroul Canessane* , S. Srinivasan**

* Research Scholoar, Sathyabama University, Tamil Nadu, India.

** Professor & Head, Department of Computer Science and Engineering, Anna University, Tamil Nadu, India.

Canessane, R. A., and Srinivasan, S. (2013). The Software Architecture Towards Performance Evolution. i-manager’s Journal on Software Engineering, 8(1), 24-34. https://doi.org/10.26634/jse.8.1.2422

Abstract

Performance of a system is one of the key factors to be considered while building a system, due to the system characteristics such as code mobility, platform heterogeneity and distributed deployment. The decisions that are taken at the time of creating software architectural design have a major impact on the resulting system. Some of the performance indices are mostly hidden. The authors have proposed a design Framework which uses an iterative method for evaluating and also transforming software architecture until the performance indices are satisfied. There are several methodologies proposed for automating the software- hardware models to a performance models. The framework provides a set of resource models, annotations and the integration procedures which supports simulation and makes the performance analysis easier. They have presented the results from a SmartHome system which shows the stability of our approach.

References

[1]. Pratima Singh and Anil Kumar Tripathi, (2012). Issues in Testing of Software with NFR, International Journal of Software Engineering & Applications (IJSEA), Vol.3, No.4, July.

[2]. Bollingger. T., Voas. J., Boasson.M. (2004). Persistent Software Attributes, IEEE Software, Nov/Dec 2004, pp.16-18.

[3]. Mehta. R, Tomás Ruiz-López, L. Chung and M. Noguera, (2013). Selecting among alternatives using dependencies: an NFR approach, SAC 13, Proceedings of the 28th Annual ACM Symposium on Applied Computing, Pages 1292-1297.

[4]. Yi Liu, Zhiyi Ma, Rui Qiu, Hongjie Chen and Weizhong Shao, (2012). An Approach to Integrating Non-functional Requirements into UML Design Models Based on NFR-Specific Patterns, Quality Software (QSIC), 12th International Conference, IEE Explore digital library, 2012.

[5]. Staehil. R., Ray. I., George. G., Ghosh. S. (2004). Aspect oriented approach to early Design Modelling, IEEE Proceedings Software, Vol 151 (4), pp. 153-185.

[6]. Balsamo. S., Marco. A. D., Inervedi. P., and Simeoni. M. (2004). Model based performance prediction in software development: A survey, IEEE Trans. Software Engg., 2004.

[7]. Yilmaz. C.,. Krishna. A.S., Memon. A., Porter. A., Schmidt. D.C., Gokhale. A. and Natarajan. B. (2005). Empirical Software Engineering: Main Effects Screening: A Distributed Continuous Quality Assurance Process for Monitoring Performance Degradation in Evolving Software Systems, Proc. Int'l Conf. Software Eng.

[8]. Surpass Series Products, Siemens Information and Comm. Network, www.siemens.com/surpass, 2007.

[9]. Cortellessa. V., P. Pierini, and D. Rossi, (2005) On the Adequacy of UML-RT for Performance Validation of an SDH Telecommunication System, Proc. Int'l Symp. Object-Oriented Real-Time Distributed Computing.

[10]. Umar. M, M. N. Ahmedkhan, (2012). A Framework to Separate Non-Functional Requirements for System Maintainability, Kuwait Journal of Science and Engineering, 39 (1B) pp.211 -231.

[11]. Romina Ermo, Vittorio Cortellessa, Alfonso Pierantonio, Michele Tucci. (2012). “Performance Driven architectural refactoring through bidirectional model Transformation”, ACM SIGSOFT Conference on Quality of Software Architectures, QoSA 2012.

[12]. Aroul canessane. R, S. Srinivasan, (2013). “A Framework for Analysing the System Quality”, ICCPCT 2013.

[13]. Krogstie. J, Sindre. G, and Jørgensen. H, (2006). “Process Models Representing Knowledge for Action: A Revised Quality Framework,” European J. Information Systems, vol. 15, no. 1, pp. 91-102, 2006.

[14]. Aldrich. J. (2008). “Using Types to Enforce Architectural Structure,” Proc. Working IEEE/Int'l Federation for Information Processing (IFIP) Conf. Software Architecture, pp. 211-220, 2008.

[15]. Brownsword. L. (2004). “Current Perspectives on Interoperability”, Software Engineering Institute, Carnegie Mellon University, PittsBurgh, 2004.

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Research Paper

A New Model For E-Business Performance Testing

P. Chitti Babu* , K.C.K. Bharathi, J. Shaik Mohamed*

* Professor & Principal, APGCCS, Rajampet, India.

Assistant Professor, Department of MCA, AITS, Rajampet, India.

* Assistant Professor, Department of MCA, SITAMS, Chittoor, India.

Babu, P, C., Bharathi, K. C.K., and Mohamed, J. S. (2013). A New Model For E-Business Performance Testing. i-manager’s Journal on Software Engineering, 8(1), 35-40. https://doi.org/10.26634/jse.8.1.2423

Abstract

The extraordinary growth in the World Wide Web has been sweeping through business and industry. By using web technologies many companies have developed or integrated their critical applications. Testing web applications become crucial, particularly as performance of web applications become complex. Performance of many Web sites depends on the load on the site at peak time under varying conditions. Performance testing is normally conducted with the help of performance testing tools in a reasonable simulated environment. As user is the ultimate judge for the success of web site. Performance perceived by the user is most crucial. Due to complexity of web site, it is not possible to draw a common denominator for user perception of the Web site and designer's viewpoint. It is necessary to analyze the users perceptions and modulate to fit into the designer's shoes in such circumstance. This paper proposes a new model, which enforces the designer to incorporate the users perception by projecting and modulating the perceptions. Later, the designer would consider the projected perceptions during the performance testing without diluting the reliability.

References

[1]. Bill Jaeger, (1999). "Minimizing Risk with Proactive Performance Testing", E-Business Advisor Article, April, 1999.

[2]. "Microsoft Site Server 3.0 Commerce Server," Capacity and Performance Analysis manual.

[3]. Pressman & Roger S, "Software Engineering: a Practionar 's Approach ", McGraw Hill International.

[4]. William Perry, (1995). "Effective Methods for Software Testing ", John Wiley, New York, 1995.

[5]. Dan Sullivan, (1999). "e-Business Application Testing in Action", e-Business Advisor, July 1999.

[6]. Subraya BM and Subrahmanya SV, (2000). "Performance Testing: A Methodical Approach to E - commerce Application", 13th International Quality Week 2000 Software Quality Week 2000 * Internet Quality Week 2000, May 30 - June 2, 2000, Hyatt Regency, Embarcadero, San Francisco, California.

[7]. Subraya BM and Subrahmanya SV, (2000). "Object driven Performance Testing in Web Applications", Proceedings of The First Asia-Pacific Conference on Quality Software, Kong, October 30-31, 2000.

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Research Paper

Software Quality Prediction Of Object Oriented Software In Successive Releases Through Multiple Classifiers

Dharmendra* , Dr. Anil Kumar Malviya**

* Research Scholar, Department of Computer Science & Engineering, Mewar Universiy, Chittorgarh Rajasthan, India .

** Associate Professor, Department of Computer Science & Engineering, Kamla Nehru Institute of Technology, KNIT, Sultanpur, U.P., India.

Gupta, D. L., and Malviya, A. K. (2013). Software Quality Prediction Of Object Oriented Software In Successive Releases Through Multiple Classifiers. i-manager’s Journal on Software Engineering, 8(1), 41-48. https://doi.org/10.26634/jse.8.1.2424

Abstract

In this paper the comparative analysis of a java based text editor jEdit and all its successive releases has been done. Different machine learning classifiers have been used to get findings in terms of Accuracy, Precision, Recall, F-measure and AUC values. Based on all performance measures it is concluded that the bugs are decreasing release by release and quality is increasing as well. The authors have used WEKA a machine learning tool for finding and analysing of the results.

References

[1]. Bouktif, S., Ahmed, F., Khalil, I. & Antoniol, G. (2010). A novel composite model approach to improve software quality prediction, Information and Software Technology, Elsevier, Vol52, pp.1298-1311.

[2]. Breiman, L. (2001). Random Forest, Machine Learning Vol.45, pp.5-32.

[3]. Catal, C. & Diri, B. (2009). A systematic review of software fault prediction studies, Expert Systems with Applications, Elsevier, Vol. 36,pp. 7346-7354.

[4]. Catal, C. & Diri, B. (2009). Investigating the effect of data set size, metrics set and features selection Techniques on software fault prediction problems, Information Sciences, Vol.179, pp. 1040-1058.

[5]. Catal, C. (2012). Performance Evaluation Metrics for Software Fault-Prediction Studies, Acta Polytechnica Hungarica, Vol. 9,no.4.

[6]. Chidamber, S. R. & Kemerer, C.F. (1994). A metrics suite for object oriented design, Software Engineering, IEEE Transactions on, Vol.20, no.6, pp.476,493, doi: 10.1109/32.295895.

[7]. Hall, T., Beecham, S., Bowes, D., Gray, D. & Comsell, S. (2011). A Systematic Review of Fault Prediction Performance in Software Engineering, in Lero: The IRISH software Engineering Research Centre, Lero Technical Report Lero-TR-2011-03.

[8]. Jureczko, M., & Madeyski, L. (2010). Towards identifying software project clusters with regard to defect prediction, Proceedings of the 6th International Conference on Predictive Models in Software Engineering (Promise-10) held at Timisoara, Romania, published in ACM, New York, USA. http://doi.acm.org/10.1145/ 1868328.1868342.

[9]. Khoshgoftaar, T.M., & Seilya, N. ( 2002). Software Quality Classification modeling using the SPRINT decision tree algorithm, Proceeding of 4th IEEE International Conference on Tools with Artificial Intelligence, Washington, D.C, pp.365-374.

[10]. Landwehr, N., Hall, M., & Frank, E. (2005). Logistic Model Tree, Machine Learning, Vol. 59, issue 1-2, pp. 161-205.

[11]. Malhotra, R., & Jain, A. (2011). Software Fault Prediction for object oriented systems: A Literature Review, ACMSIGSOFT Software Engineering Notes, Vol. 36, no 5.

[12]. Pai, G.J. & Dugan, J. B. (2007). Emperical analysis of software fault content and fault proneness using Bayesian methods, IEEE Transactions on Software Engineering, Vol. 33,pp. 675-686.

[13]. Pressman, R.S. (2005). Software Engineering - A Practioner's Approach, Sixth Edition, McGraw Hill Publication.

[14]. Quinlan, J.R. (1993). C 4.5:Programs for Machine Learning, Morgan Kaufman Publishers.

[15]. Tian, J. (2005). Software Quality Engineering, Wiley India Edition.

[16]. Twala, B. (2011). Software Fault Prediction Using Multiple Classifiers, in IEEE.

[17]. WEKA:http//www.cs.waikato.ac.nz/ml/weka.

i-manager's Journal on Software Engineering (JSE)

Current Issue Vol. 19 Issue 2

Most Read

Most Cited

Volume 8 Issue 1 July - September 2013

Role Of Software Re-Engineering Through C To Java Conversion

Vinod Kumar Yadav* , Ajay Pal Singh**, Satendra Kumar***

* Assistant Professor, Department of Computer Science and Engineering, SLSET, U.K., India. ** Assistant Professor, Department of Computer Science and Engineering, DBIT, UP, India. *** Assistant Professor, Department of Computer Science and Engineering, SITM, Haryana, India.

Yadav, V. K., Singh, A. P., and Kumar, S. (2013). Role Of Software Re-Engineering Through C To Java Conversion. i-manager’s Journal on Software Engineering, 8(1), 1-6. https://doi.org/10.26634/jse.8.1.2420

Abstract

References

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Performance Evaluation Of Fuzzy Voters Used In Safety Critical Systems

Phani Kumar* , SeethaRamaiah Panchumarthy**, Anu A.Gokhale***

Singamsetty, P., Panchumarthy, S., and Anu A. Gokhale, A. A. (2013). Performance Evaluation Of Fuzzy Voters Used In Safety Critical Systems. i-manager’s Journal on Software Engineering, 8(1), 7-23. https://doi.org/10.26634/jse.8.1.2421

Abstract

References

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The Software Architecture Towards Performance Evolution

R. Aroul Canessane* , S. Srinivasan**

* Research Scholoar, Sathyabama University, Tamil Nadu, India. ** Professor & Head, Department of Computer Science and Engineering, Anna University, Tamil Nadu, India.

Canessane, R. A., and Srinivasan, S. (2013). The Software Architecture Towards Performance Evolution. i-manager’s Journal on Software Engineering, 8(1), 24-34. https://doi.org/10.26634/jse.8.1.2422

Abstract

References

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A New Model For E-Business Performance Testing

P. Chitti Babu* , K.C.K. Bharathi**, J. Shaik Mohamed***

* Professor & Principal, APGCCS, Rajampet, India. ** Assistant Professor, Department of MCA, AITS, Rajampet, India. *** Assistant Professor, Department of MCA, SITAMS, Chittoor, India.

Babu, P, C., Bharathi, K. C.K., and Mohamed, J. S. (2013). A New Model For E-Business Performance Testing. i-manager’s Journal on Software Engineering, 8(1), 35-40. https://doi.org/10.26634/jse.8.1.2423

Abstract

References

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Software Quality Prediction Of Object Oriented Software In Successive Releases Through Multiple Classifiers

Dharmendra* , Dr. Anil Kumar Malviya**

* Research Scholar, Department of Computer Science & Engineering, Mewar Universiy, Chittorgarh Rajasthan, India . ** Associate Professor, Department of Computer Science & Engineering, Kamla Nehru Institute of Technology, KNIT, Sultanpur, U.P., India.

Gupta, D. L., and Malviya, A. K. (2013). Software Quality Prediction Of Object Oriented Software In Successive Releases Through Multiple Classifiers. i-manager’s Journal on Software Engineering, 8(1), 41-48. https://doi.org/10.26634/jse.8.1.2424

Abstract

References

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Vinod Kumar Yadav* , Ajay Pal Singh, Satendra Kumar*

* Assistant Professor, Department of Computer Science and Engineering, SLSET, U.K., India.

Assistant Professor, Department of Computer Science and Engineering, DBIT, UP, India.

* Assistant Professor, Department of Computer Science and Engineering, SITM, Haryana, India.

Phani Kumar* , SeethaRamaiah Panchumarthy, Anu A.Gokhale*

* Research Scholoar, Sathyabama University, Tamil Nadu, India.

** Professor & Head, Department of Computer Science and Engineering, Anna University, Tamil Nadu, India.

P. Chitti Babu* , K.C.K. Bharathi, J. Shaik Mohamed*

* Professor & Principal, APGCCS, Rajampet, India.

Assistant Professor, Department of MCA, AITS, Rajampet, India.

* Assistant Professor, Department of MCA, SITAMS, Chittoor, India.

* Research Scholar, Department of Computer Science & Engineering, Mewar Universiy, Chittorgarh Rajasthan, India .

** Associate Professor, Department of Computer Science & Engineering, Kamla Nehru Institute of Technology, KNIT, Sultanpur, U.P., India.